How to Register Japanese RCTs with The Cochrane Library/CENTRAL.
Bibliographic record
Abstract
Background: Evidence-based medicine (EBM) is now a trend, but its information infrastructure has not been well developed in Japan, as seen by the fact that The Cochrane Library/CENTRAL does not list enough Japanese randomized controlled trials/controlled clinical trials (RCTs/CCTs). This situation should be improved.Objective: To describe the Database of Japanese Randomized Controlled Trials (J-RCT) project, funded by the Ministry of Education, Science, Sports and Culture of Japan; to register citations in English of Japanese RCTs with The Cochrane Library/CENTRAL.Method: RCTs/CCTs citations in the journal Rinsho Hyoka (Clinical Evaluation) from 1982 to 1995 have been handsearched according to the handsearch manual of the Cochrane Collaboration and processed into a ProCite file utilizing the JICST-E file of the Japan Science and Technology Corporation (JST), then sent to the New England Cochrane Center.Result: 119 citations were registered with The Cochrane Library 1999 Issue 3, and a data processing system has been developed from the JICST-E file, including their problem-solving methods and future perspectives.Discussion: The role of J-RCT and the significance of cooperation with JST have been recognized. This project is expected to encourage Japanese medical journals to improve their reporting of clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.451 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.073 | 0.069 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.110 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".